CLASSIFICATION AND ASSESSMENT OF THE LAND USE - …...CLASSIFICATION AND ASSESSMENT OF THE LAND USE - LAND COVER CHANGES IN JODHPUR CITY USING REMOTE SENSING TECHNOLOGIES Madhur Aditya

CLASSIFICATION AND ASSESSMENT OF THE LAND USE - LAND COVER CHANGES

IN JODHPUR CITY USING REMOTE SENSING TECHNOLOGIES

Madhur Aditya Saharan1 , Nikhil Vyas2, S.L. Borana3, * , S.K. Yadav3

1Mahindra École Centrale College of Engineering, Hyderabad, India - [email protected]

2 Indian Institute of Information Technology, Surat, India - [email protected] 3DL, Jodhpur, India - [email protected], [email protected]

Commission V, SS: Infrastructure and Development Planning

KEY WORDS: Remote Sensing, LULC Classification, Landsat, Assessment, Change Detection.

ABSTRACT:

Land Use - Land Cover (LULC) classification mapping is an important tool for management of natural resources of an area. The

remote sensing technology in recent times has been used in monitoring the changing patterns of land use-land cover. The aim of the

study is to monitor the LULC changes in Jodhpur city over the period 1990 - 2018. Satellite imagery of Landsat 8 OLI (June, 2018)

& Landsat TM (Oct, 1990) were used for classification analysis. Supervised classification-maximum likelihood algorithm is used in

ENVI software to detect land use land cover changes. Five LULC categories were used, namely- urban area, mining area, vegetation,

water bodies and other area (Rock outcrops and barren land). The LULC classified maps of two different periods i.e. 2018 and 1990

were generated on 1:50,000 scale. The accuracy assessment method was used to measure the accuracy of classified maps. This study

shall be of good assistance to the town planners of Jodhpur city for the purpose of the sustainable development as per the master plan

2031.

* Corresponding author

1. INTRODUCTION

Remote sensing (RS) is in general defined as the process of

acquiring information about an object, area or phenomenon

without being in physical contact with it (Lillesand et al.,

2004; Campbell, 2002). Currently the term is used for satellites

and even aircrafts carrying sensors in acquiring information

regarding land surface, oceans, glaciers, air currents, etc.

Land use refers to the human induced changes for agricultural,

industrial, residential or recreational purposes and Land cover

features that are present on the earth‘s surface (Ramachandra

and Bharath, 2012). Jodhpur is the second largest city of

Rajasthan with population of 1.05 million. Over the years the

city landscape saw significant changes due to growth of core

and ancillary industries in north and south west part. The

urbanization and availability of higher education opportunities

facilitate immigration of labors and qualified professionals.

These immigration population creates challenges to the local

city planners for creating new public amenities with all urban

facilities based on 2031 master plan. Geographical techniques

are proven tools for mapping and monitoring land use for

proper classification and assessment. In recent years, most

urban land-use land cover studies have employed data from

Landsat satellite (Herold et al., 2002). Classification of Landsat

images of Jodhpur, India of two different years 1990 and 2018

have been incorporated in this study of LULC. Landsat series

satellites were first launched in 1972 with Landsat 1 which was

de-orbited in 1978. Eight Landsat satellites have been used for

GIS studies and currently 3 satellites, such as Landsat 5, 7 and

8, are in orbits. Description of spectral bands and ground

resolution of Landsat OLI is shown in Table-1. The images

formed by satellites have various bands and ENVI software

allows in forming various band combinations. Every band has

provided with different colours, wavelength and resolution

assigned for this LULC study (Table-2).

For this study, ENVI (Environment for Visualizing Images)

Software is used to classify different land cover features. ENVI

is used for visualizing, analysing, and presenting different type

of digital satellite images. Image-processing application is

helpful in advanced, spectral tools, geometric correction, terrain

analysis, radar analysis, raster and vector GIS capabilities.

2. STUDY AREA

The city of Jodhpur was considered for this research. Jodhpur

city is located in North West region of India in state of

Rajasthan. The spatial location of city is 26º18' N latitude and

73º04' E longitude and an average altitude of 224m above mean

sea level. The Study Area comprises of approx. 600 sq km

(Fig.1).

Figure 1. Satellite Image of Study Area.

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-5, 2018 ISPRS TC V Mid-term Symposium “Geospatial Technology – Pixel to People”, 20–23 November 2018, Dehradun, India

This contribution has been peer-reviewed. https://doi.org/10.5194/isprs-archives-XLII-5-767-2018 | © Authors 2018. CC BY 4.0 License.

767

Band Number Wavelength(µm) Resolution Colour

1 0.433 - 0.453 30 Coastal

2 0.45 - 0.515 30 Blue

3 0.525 - 0.6 30 Green

4 0.63 - 0.68 30 Red

5 0.845 - 0.885 30 NIR

6 1.56-1.66 60 SWIR

7 2.1-2.3 30 SWIR

8 0.5- 0.68 15 PAN

9 1.36-1.39 30 Cirrus

10 10.6-11.2 100 Thermal

11 11.5-12.5 100 Thermal

Table 1. Spectral bands and resolution of Landsat OLI.

Band Combinations R G B

Natural Colour 4 3 2

False Colour (Urban) 7 6 4

Colour Infrared (Vegetation) 5 4 3

Agriculture 6 5 2

Atmospheric penetration 7 6 5

Healthy Vegetation 5 6 2

Land/Water 5 6 4

Vegetation Analysis 6 5 4

Table 2. Available bands combinations for various

applications.

3. MATERIALS AND METHODOLOGY

3.1 Materials

Two Data sets were used in this research. Satellite data that

comprised of two years multi - temporal satellite imageries

Landsat TM imageries of 1990 and Landsat OLI 8 of 2018

(Table-3, Figure-2 and 3) acquired from the USGS GLOVIS

website. Secondary data incorporated are the ground truth (GT)

data for the LULC feature classes (Table-4). The GT data were

in the form of location points collected using GPS for the image

analysis and used for classification analysis and assessment of

classification accuracy (Jensen et al. 2005).

Satellites/

Sensors

Resolution Path/Row Acquisition Date

Landsat TM 30m 149/42 29-10- 1990

Landsat TM 30m 149/42 01-06- 2018

Table 3: Characteristics of Satellite Data Used.

LULC Classes Description

1 Urban area Commercial, residential, industrial and

transportation infrastructures.

2 Vegetation Describes areas with Forest, grass, trees

and shrubs trees

3 Mining

Area

Sandstone Mining quarry of clusters.

4 Water

bodies

Areas covered with River, open water,

lakes, ponds and reservoirs.

5 Rock

outcrops

features such as rocks hillocks.

6 Other Area This class defines Crop fields, fallow lands,

barren area.

Table 4: LULC classification scheme.

Figure 2. Satellite Image of Study Area

(Landsat TM 1990)

Figure 3. Satellite Image of the Study Area.

(Landsat OLI 2018)

3.2 Methodology

As mentioned earlier, the objective of this paper is to compare

land cover features of Jodhpur in two different years 1990 and

2018. Two Landsat image were taken, and results were recorded

and compared simultaneously. Methods of finding result were

same for each image and the procedure followed was:



768

Different bands are used in formation of image. All the

bands were imported in the software with the help of new

file builder, where all the bands got reordered to avoid any

error in getting results.

Georeferenced images are usually considered for analysing

remote sensing, due to its application like calculating areas

or finding the position on globe. Registration tool is used

for georeferencing image. Ground control points (GCPs)

are selected from Image windows or Vector windows, and

further these points are used in Image-to-Map to select

GCPs for image-to-map registration.

Then images were resized with the help of Resizing Data

(Spatial/Spectral). The function of the tool is to resize the

image and/or perform image spatial or spectral subsetting,

as well as use the resize function to create new images of

any size or aspect ratio. Samples and Line of image are

picked to get the image of required aspect ratio.

Then ROIs (Regions of interest) are created. ROIs are

portions of images, either selected graphically or by

thresholding. The regions can be irregularly-shaped and

was used in extracting statistics for classifying.

Once ROIs are created, maximum likelihood function is

used. Maximum likelihood classification is used for

calculating the probability of a particular pixel assigned to

a given class which defines LULC. Maximum likelihood

classification assumes the statistics for every class in each

band are normally distributed. Maximum likelihood

classification is calculated for each pixel in the image by

following discriminant functions (Richards, 1999):

- - ½ -

Where: i = class

x = n-dimensional data (where n is the number of bands)

ƿ (ω) = probability that class occurs in the image and is

assumed the same for all classes

| | = determinant of the covariance matrix of the data in

class

= its inverse matrix

= mean vector

Select classification output to File or Memory.

Once after getting results from maximum likelihood, we

need to open the result image. Once we get that image, one

need to go to overlay and after selecting overlay

classification tool is chosen. We get interactive class tool

input file option. Then chosen the image and interactive

class tool appears. All the classes we need to get results of

are selected and we get area of the region.

GCP Points were collected for verification of doubtful

areas. Based on the GCPs, the misclassified areas were

corrected using ENVI software. The error matrix and

Kappa methods were used to evaluate the classification

mapping accuracy (Rawat and Kumar,2015; Yadav and

Borana, 2017).

4. RESULTS AND DISCUSSION

The study area has seven LULC categories, namely: urban area,

mining area, vegetation, water bodies, rock outcrops and other

area (Borana et al., 2017). The LULC Classifications results

for 1990 and 2018 are illustrated through Figure 3 and 4. Over

twenty-eight years (1990-2018), the changes in area coverage

varied from one LULC class to another is shown in Table-5-7.

Change detection is made possible by Remote Sensing

technology in less time and with better accuracy (Kachhwala,

1985). Accuracy assessment of the LULC classification results

were measured using error matrix and Kappa methods, an

overall accuracy of 871.18% for 1990 and 89.22% for 2018.

The Kappa coefficients for year 1990 and year 2018 maps were

0.801 and 0.892 respectively.

Class Name Npts Pct Total Area

Urban Area [35596] 5.269% 28.615 Km²

Vegetation [13782] 8.828% 45.07 Km²

Mining Area [46236] 7.25% 37.172 Km²

Water bodies [52611] 2.927% 14.943 Km²

Rock outcrops [58639] 9.020% 47.142 Km²

Other area [428173] 66.705% 344.228 Km²

Total [5,76,398] 100% 510.535 Km²

Table 5. Land cover/land use classes and area (1990)

Class Name Npts Pct Total Area

Urban [104643] 18.309% 135.765 Km²

Water body [4965] 0.869% 8.483 Km²

Vegetation [84045] 14.705% 75.146 Km²

Mining Area [11990] 2.098% 10.721 Km²

Rock outcrops [47009] 8.225% 46.166 Km²

Other area [318884] 55.794% 286.564 Km²

Total [571539] 100% 511.029 Km²

Table 6. Land cover/land use classes and area (2018)

Class Name Change in Area Change in

Percentage*

Urban 107.15 Km² (↑) 13.04 % (↑)

Water Body 6.46 Km² (↓) 2.058% (↓)

Vegetation 30.076 Km² (↑) 5.877% (↑)

Mining Area 26.451 Km² (↓) 5.152% (↓)

Rock outcrops 0.976 Km² (↓) 0.795% (↓)

Other Area 57.664 Km² (↓) 10.911% (↓)

(↑) Indicates increase; (↓) Indicates decrease

*Change in percentage with respect to total area

Table-7. Land cover/land use classes and change detection

(1990 to 2018)



769

Figure 4. Classified map outputs (1990).

Figure 5. Classified map outputs (2018).

It can be learnt from figures 4, 5 and tables 4 to 6 that the urban

sprawl has increased by 13.04% and vegetation cover has also

increased by 5.9%. The increase in the mining area is due to

vegetation over old mining remnants. The mining area is

decreased by 5.2% but it has shifted from one side of the city to

the other side. The mining area and surrounding urban sprawl

has inadequate vegetation cover due to active mining. A

judicious mine closure plan can positively change LULC and

benefit urban planning of the case study. Parihar (1993) has

extensively worked for mine closure plans and suggestive

measures of his study can be implemented in augmenting LULC

cover. The city administration can notice inadequacy in

vegetation area and water bodies. A systematic mining activity

can resolve both the issue of vegetation and water storage

bodies. The outputs from the remote sensing shall be useful in

the implementation of AMRUT policy for urban growth of

Jodhpur city. Also updated data also be useful in land use

planning for achieving sustainable development for present and

future demands of the city.

5. CONCLUSIONS

RS output has frequently been used to help in the policies

making and provides land use-land cover patterns. The analysis

techniques are used to map LULC change at landscape level. In

Jodhpur City, Urban area and Vegetated area has increased and

there has been decrease in other areas such as Rock outcrops,

mining area, water bodies and other area. Urban area in 1990

was 28.615 Km² or 5.269% of the entire area and in 2018 is

135.765 Km². Vegetated area in 1990 was 45.07 Km² or

8.828% of the entire area and in 2018 is 14.705% of the entire

area. Water Body in 1990 was 14.943 Km² or 2.927% of the

entire area and in 2018 is 8.483 Km² or 0.869% of entire area.

Rock outcrops area in 1990 was 47.142 Km² or 9.020% of the

entire area and in 2018 is 46.166 Km² or 8.225% of entire area.

Mining area in 1990 was 37.172 Km² or 7.25% of the entire

area and in 2018 is 10.721 Km² or 2.098% of entire area. Other

area in 1990 consisted of 344.228 Km² or 66.705% of the entire

area and in 2018 is 286.564 Km² or 55.794% of entire area. The

present study shows that remote sensing is an important

technology for LULC temporal analysis and classification

mapping. Change detection is made possible by this technology

in less time and with better accuracy. The data generation will

be useful for the town planning development for proper land

planning with required infrastructure in each land development

area of the city.

ACKNOWLEDGEMENT

The authors are thankful to the Director Defence Laboratory,

Jodhpur for his help during the study. The authors are also

thankful to Head Remote Sensing Group for his critical

suggestions and encouragement. The first two authors sincerely

acknowledge Defence Laboratory for generously extending

facilities for their summer internship from respective

educational institutes.

REFERENCES

Herold M, Scepan J, Clarke K (2002) The use of remote sensing

and landscape metrics to describe structures and changes in

urban land uses. Environment and Planning 34: 1443-1458.

Richards, J.A., 1999, Remote Sensing Digital Image Analysis,

Springer-Verlag, Berlin, p. 240.

Lillesand TM, Kiefer RW, Chipman JW (2004) Remote Sensing

and Image Interpretation. John Wiley & Sons, New Jersey,

USA.



770

Bhagawat Rimal (2011). Urban Growth and Land Use /Land

Cover Change of Pokhara Sub-Metropolitan City, Nepal.

Journal of Theoretical and Applied Information Technology,

Vol.26, No. 2, ISSN 1992-8645.

Borana, S.L.,Yadav,S.K and Parihar, S.K., Using Remote

Sensing and GIS to Monitor Land Use-Land Cover Change in

Jodhpur city and Surrounding Area, Int. J. of Innovative

Research in Science, Engineering and Technology, Vol. 6, Issue

10, 2017, Page No.20369- 20375.

Campbell, J., B. 2002. Introduction to Remote Sensing - third

edition. Taylor and Francis, London. p. 621.

Jensen, R.,J. Gatrell, and D. McLean. 2005. Geospatial

Technologies in Urban Environments. New York : Springer.

Setturu Bharath, & Ramachandra, T. V. (2012). Landscape

dynamics of Uttara Kannada district. LAKE 2012: National

Conference on Conservation and Management of Wetland

Ecosystems.

Parihar, S.K. (1993) Land use planning of sandsstone quarrying

area around Jodhpur, M.E. Thesis, J.N. Vyas University,

Jodhpur.

Yadav, S.K and Borana, S.L., Change Detection Analysis of

Urban Land Use Using IRS L-3 Data of Jodhpur City and

Surrounding Area, Int. J. of Informative & Futuristic Research,

Volume 5 Issue 3, 2017, Page No. 8872-8877.

T.V. Ramachandra, Bharath H. Aithal, Spatio-Temporal Pattern

of Landscape Dynamics in Shimoga, Tier II City, Karnataka

State, India, International Journal of Emerging Technology and

Advanced Engineering , Volume 2, Issue 9, September 2012.

J.S. Rawat and Manish Kumar, Monitoring land use/cover

change using remote sensing and GIS techniques: A case study

of Hawalbagh block, district Almora, Uttarakhand, India, The

Egyptian Journal of Remote Sensing and Space Sciences (2015)

18, 77–84.

Kachhwala, T.S., 1985. Temporal monitoring of forest land for

change detection and forest cover mapping through satellite

remote sensing. In: Proceedings of the 6th Asian Conference on

Remote Sensing. National Remote Sensing Agency, Hyderabad,

pp. 77–83.

Borana S. L. and Yadav S.K., Accuracy Assessment of Land

Cover Classification in Jodhpur City Using Remote Sensing and

GIS, Int. J. of Advanced Research in Computer and

Communication Engineering, Vol. 6, Issue 10, 2017, Pg .220-

224.



771

Documents

CLASSIFICATION AND ASSESSMENT OF THE LAND USE - …...CLASSIFICATION AND ASSESSMENT OF THE LAND USE - LAND COVER CHANGES IN JODHPUR CITY USING REMOTE SENSING TECHNOLOGIES Madhur Aditya